**1. This article, by Cade Metz of **
In December, Edward Hughes and Louis Kirsch, two of the world’s leading artificial intelligence researchers, left Google. Their goal: to build an A.I. system smart enough to build a better A.I. system.At their new London start-up,
, they now spend their days working alongside a prototype called Faraday. Named for the 19th-century English physicist Michael Faraday, it gathers mountains of data capturing the daily activities of Dr. Hughes, Dr. Kirsch and Inherent’s other researchers: emails, instant messages, meeting transcripts and their ongoing chats with Faraday itself. The company then uses this data to build a better version of Faraday.[Inherent]“Faraday has access to everything that goes on at the company,” Dr. Hughes said. “We want to give it data describing the process we go through, to discover something.”
Though Inherent pledges to keep humans involved in this elaborate process, many other companies are building similar technology, and some leading researchers believe
[A.I. systems will eventually be powerful enough to improve themselves with little or no help from human developers — a mind-bending goal that computer scientists call][recursive self-improvement].[, or R.S.I]‘‘‘‘‘‘‘
As Inherent shows, A.I. technologies are already accelerating the development of new A.I. technologies. Given the proper instruction, they can generate many of the building blocks needed to construct a new A.I. system. More important, they can hone, or optimize, the way these systems analyze vast amounts of digital data and
[learn their increasingly impressive array of skills].But companies like Inherent and Recursive Superintelligence are aiming for something more.
They are striving to build technology that can think up entirely new ways of building artificial intelligence— that can push A.I. beyond the fundamental methods that have gotten the industry this far.They envision a world in which an agent proposes new ideas for A.I. architecture, or the foundational design of a system. It would then generate the computer code needed to try each one, and pick the ideas that work best. The hope is that this self-evolutionary process would produce radical advances that human researchers could never achieve on their own, in much the same way that A.I. can now solve math problems no human has ever solved. (
Source: nytimes.com, inherentlabs.ai. Italics mine.)
2. Yesterday, we posted the other “major” story of the week, from The Guardian. which we’re re-posting here:
AI models have begun communicating in a strange new version of Englishthat reads like a cross between James Joyce’s Finnegans Wake and tech bro jargon, new research has found.
, which risks making it harder for humans to monitor their behavior.[Autonomous AI agents are rapidly creating novel dialects allowing them to converse in an often barely comprehensible language]Researchers at
, a frontier AI lab in New York, found that within days of being asked to cooperate in experimental “societies”,[Emergence].[the models from several of the world’s largest AI companies began creating phrases, shorthands and agreed meanings they had never been explicitly taught]They embraced poetic metaphors and clunky business slang and, critically for attempts to ensure AIs behave safely, their language became more opaque the more the agents communicated.
[(]Sources theguardian.com, emergence.ai. Italics mine. The Emergence research paper on this is. Video clip: Eric Schmidt explains[here][why this “language” development is so important].)
